Content preference with ratings and suggestions system and methods

ABSTRACT

A system for determining preference, including a client device with feedback controls, a server and addressable URIs; the device and server in communication over an electronic network and the URIs&#39; content retrievable over the network; the server automatically receiving and storing ratings, tracking URIs, using ratings to create a preference model for URIs, and using the preference model to suggest URIs; the stored ratings include a record having a rated item URI, a rater having a unique identification, a rating value provided by the rater, and at least one metadatum for creating subsets of ratings. Also, a method for automatically creating a chimeric preference vector, the method steps including identifying a multiplicity of datasets of rated items; automatically combining the datasets to form a combined dataset; automatically identifying ratings collisions; treating ratings collisions to form a data subset; and generating a chimeric preference vector based on the data subset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This nonprovisional utility patent application claims the prioritybenefit of the prior filed copending Korean nonprovisional applicationnumber 10-2011-0056395, filed Jun. 10, 2011.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to online surveying to determine userpreferences.

2. Description of the Prior Art

Prior art survey administration approaches and tools in-person, bytelephone, hybrid methods (e.g.—using TV, radio, webcast to deliveritems and using phones or SMS for submitting responses),self-administered (mail), self-administered (web apps via browsers).

Weaknesses in current approaches and tools include costs, speed,complexity, insufficient psychographic data, ineffective rewards,copyright/trademark infringement, lack of incorporation of socialnetworking, and lack of optimization for mobile devices.

Prior art survey approaches are expensive because they require expertsto design the survey and decide whom to target, and require a contractwith an existing respondent pool or a recruitment effort to getsufficient respondents. Across current methods, administration costsstill increase roughly in-line with the number of respondents required.

In-person administration has high costs per potential respondentcontacted. Phone and hybrid methods tend to be cheaper per potentialrespondent contacted, but convert potential respondents into actualrespondents at a lower rate. While self-administered methods tend to besubstantially cheaper per potential respondent contacted than in-person,phone, or hybrid methods, they also have substantially lower conversionrates—forcing a much larger number of potential respondents to becontacted to achieve statistically significant response rates.

In-person recruitment is slow but has a relatively high response ratefrom potential respondents contacted when compared to other methods. Byphone recruitment is also slow and requires many potential respondentsto be contacted for each completed survey.

Self-administration—in all forms—has an even lower response rate fromall potential respondents contacted than supervised methods, and isprone to unpredictable delays in completion for those respondents who docomplete the surveys.

Survey design can be complex and difficult, requiring expert assistance.However, even when the survey design is very simple, administeringsurveys in a supervised context without substantially biasing responsesrequires domain-specific expertise. Methods of deliveringself-administered surveys over broadcast media and or electronicnetworks can require technical expertise unrelated to survey design oranalysis.

Demographic data is required for most surveys. While some surveys stillcollect the required demographic data from respondents as part of eachsurvey administered, some newer survey administration approachespreserve collected demographic data for respondents. If the data ispreserved for a respondent, they do not need to be asked for it againwhen they take additional surveys in the future. This can reduce thetime required to take surveys, and increase response rate amongst repeatrespondents.

Stored demographic data for past respondents can also improve theefficiency of recruitment efforts for future surveys with pre-emptivedemographic targeting. For example, potential respondents whosedemographics are already well represented in responses for a surveyalready in progress can be excluded from additional recruitment efforts.

With demographic data, there is a basic canon, including age, sex,ethnicity, location, education, profession and income. Once all this iscollected, the net benefit of storing more or more detailed demographicinformation for a respondent drops off. These deeper demographic detailsmay be important for a particular survey, but are unlikely to be broadlyuseful in recruitment for or analysis of future surveys.

Psychographic—or IAO (Interests, Activities and Opinions)—data is oftencollected as part of a survey. However, it is usually only collected ina tightly focused area specific to the survey administered. Therespondent IAO data is used in the analysis of that survey, but is notstored in a way that associates the responses with the respondent forfuture reference. The IAO data can thus be considered episodic—which isto say it is only collected and used in the context of a single survey.This omission prevents past responses from being used as an aid inrecruitment (psychographic targeting) and or analysis (correlation withpast opinions without repeating the questions) for future surveys.

In contrast to demographic data, psychographic data tends to become morebroadly useful—particularly for recruitment targeting—the more of it isavailable. The usual limitation on its collection is that longer surveystend to have correspondingly lower response rates.

Prior art enticements and rewards tend to be non-dynamic and potentiallybiasing. Enticements must be revealed during recruitment—prior to surveyadministration—to influence whether a potential respondent will chooseto participate in a survey. Enticements to participate in a survey tendto be generic (e.g. cash equivalents like AmEx gift cards) and uniform.When enticements are non-generic (i.e.—an item or service from anidentifiable brand), they risk biasing the survey—both in terms ofinfluencing who will agree to take the surveys and what opinions theymight have regarding the brand of the gift or related brands. Whenenticements are non-uniform, the mechanics of the administration becomemore complex and the costs per response with current methods tend torise because more experienced administrators are required for supervisedadministration methods and more complex automated systems are requiredfor current hybrid and self-administered methods.

However, uniform enticements miss out on opportunities to adjustincentives based on potential or actual respondents matching targetedcriteria. Easy-to-recruit demographics can be offered lower valuerewards for participation, lowering overall administration costs.Hard-to-recruit demographics can be offered higher value rewards forparticipation, increasing response rates. Respondents with keydemographic, psychographic or social networking characteristics(e.g.—having many friends, having a high propensity for sharing links,liking a particular organization, etc) can be offered “bonus” rewardseither prior to survey administration as extra enticements toparticipate or after having completed a survey to improve theirperception of a brand or organization. Conditional bonus rewards couldalso be offered as an incentive to take additional steps immediatelyupon survey completion. This allows surveys to be used as a way tocamouflage what is essentially a targeted brand promotion message.

Survey construction can be constrained by trademark and or copyrightlimits on usage of brand-specific images or language. Usage of suchcontent without permission in a printed survey can lead to objectionsfrom rights holders. Restrictions on usage of such content on web pagesgenerally fall into three categories based on the method of inclusionand how much that method modifies the context of the content from thatin which it was originally offered. Linking (i.e.—providing a hypertextlink that can trigger the display of the external content in itsoriginal form) is generally permitted without prior permission. Framing(i.e.—the inclusion of external content within a web page such thatstandard browsers render both the page content and the external contenttogether) is less clearly allowed, with one court finding that framingwas a copyright infringement because the process resulted in anunauthorized modification of the linked site. (Futuredontics Inc. v.Applied Anagramic Inc., 45 U.S.P.Q. 2d 2005 (C.D. Cal. 1998). Inlining(i.e.—direct inclusion of external content in another web page mixed inwith a given page) is usually considered more likely to be infringingthan framing as the context has been even more clearly modified from itsuse on its site of origin. So, inclusion of external, rights-protectedcontent in a web survey either via framing or inlining is likely toraise objections from the rights holders. As such, web-based surveyadministration tools which use web page UI elements (buttons, fields,etc) to collect responses are likely to raise objection when the surveyitems include trademarked or copyrighted images or phrases includedeither via framing or inlining. However, providing a survey—whetherprinted or in web form—that has links to external sites, where therights-protected content can be viewed in its original form, should notrequire any prior permission and is unlikely to raise objections. Whileunlikely to be infringing, this makes taking the survey much morecumbersome, requiring the respondent to enter URLs or click back andforth between the external content and the page where their response iscollected.

Taken together, the preceding limitations make constructing printed orweb-based surveys collecting responses on icons, web sites or slogansfrom competing brands likely to be either potentially objectionable orunduly cumbersome.

Current survey administration systems do not use social networking andmedia tools as well as they could. Most current approaches do not usethese tools at all. In addition to simplifying and automating thecollection and or confirmation of demographic information, social graphdata (personal and business connections) could be gathered as well asadditional psychographic data (likes, dislikes, shared links). Thisadditional information could be used to target potential respondentsmuch more accurately, to identify tastemakers who are more likely toconvince others to take surveys if they are so convinced, and toincentivize those more likely to be influential in recruitment morestrongly. Current approaches do none of these things.

Current survey administration systems are not optimized forparticipation via mobile devices. While there have been methodsdescribed involving combinations of broadcast media (radio, TV) to sendthe questions out with mobile phones and or text messaging devices usedby respondents to send their responses back, these require allrespondents to be watching or listening to the questions at the sametime—a requirement that limits the potential respondent pooldramatically.

Other described methods of mobile survey administration rely entirely ontext messages, both to send out the questions and for respondents toreturn their responses. However, confining surveys to questions that canbe delivered as text messages is substantially limiting, preventingquestions involving images, for example.

While some web-based self-administered surveys can be taken viaweb-enabled mobile devices, the small form factor and limited user inputmethods common to these devices make taking these surveys substantiallymore cumbersome, driving response rates on these surveys for mobileusers much lower.

While many mobile devices are capable of receiving “push notifications”(e.g.—email, text messages, alerts), these are not being used by currentsurvey administration systems to make targeted users aware of newlyavailable surveys they are likely to be interested in taking.

Collaborative filtering (CF) is the process of filtering for informationor patterns using techniques involving collaboration among multipleagents, viewpoints, data sources, etc. Applications of collaborativefiltering typically involve very large data sets. Collaborativefiltering methods have been applied to many different kinds of dataincluding sensing and monitoring data—such as in mineral exploration,environmental sensing over large areas or multiple sensors; financialdata—such as financial service institutions that integrate manyfinancial sources; or in electronic commerce and web 2.0 applicationswhere the focus is on user data, etc.

Collaborative filtering is a method of making automatic predictions(filtering) about the interests of a user by collecting tasteinformation from many users (collaborating). The underlying assumptionof the CF approach is that those who agreed in the past tend to agreeagain in the future. For example, a collaborative filtering orrecommendation system for television tastes could make predictions aboutwhich television show a user should like given a partial list of thatuser's tastes (likes or dislikes). Note that these predictions arespecific to the user, but use information gleaned from many users. Thisdiffers from the simpler approach of giving an average (non-specific)score for each item of interest, for example based on its number ofvotes.

Factor Analysis (FA) is an approach for building a preference model thatrequires far fewer calculations to make a suggestion as compared tocompeting approaches. Factor analysis is a statistical method used todescribe variability among observed variables in terms of a potentiallylower number of unobserved variables called factors. In other words, itis possible, for example, that variations in three or four observedvariables mainly reflect the variations in a single unobserved variable,or in a reduced number of unobserved variables. Factor analysis searchesfor such joint variations in response to unobserved latent variables.The observed variables are modeled as linear combinations of thepotential factors, plus “error” terms. The information gained about theinterdependencies between observed variables can be used later to reducethe set of variables in a dataset. Factor analysis originated inpsychometrics, and is used in behavioral sciences, social sciences,marketing, product management, operations research, and other appliedsciences that deal with large quantities of data.

Factor Analysis has been used with Collaborative Filtering to generateuser preference models and vectors (See Collaborative Filtering withPrivacy via Factor Analysis by John Canny. url:www.cs.berkeley.edu/˜jfc/′mender/sigir.pdf).

SUMMARY OF THE INVENTION

The present invention relates to online surveying to determine userpreferences.

It is an object of this invention to provide an online system fordetermining contextually specific preferences.

It is an object of this invention to provide a method for automaticallycreating a chimeric preference vector.

Yet another object of this invention is to provide a method foridentifying new market position content.

Accordingly, a broad embodiment of this invention is directed to asystem for determining preference, the system including a client, serveraddressable URIs and a ratings database, the ratings database includingat least one record having a rated item, a rater, at least one ratingvalue and at least one metadatum.

Another broad embodiment of this invention is directed to a method forcreating a chimeric preference vector, the method steps includingproviding a system for determining preference, identifying amultiplicity of datasets of rated items to be used; automaticallycombining the datasets to form a combined dataset; automaticallyidentifying rating collisions—cases where particular items are rated inmore than one of the combined datasets; automatically resolving ratingcollisions to generate a treated data subset of the combined dataset;and generating a chimeric preference vector based on the treated datasubset.

These and other aspects of the present invention will become apparent tothose skilled in the art after a reading of the following description ofthe preferred embodiment when considered with the drawings, as theysupport the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example graphical user interface for a content browsinginterface according to the invention.

FIG. 2 is another example graphical user interface for a contentbrowsing interface according to the invention.

FIG. 3 is an example survey feedback control set according to theinvention.

FIG. 4 is another example survey feedback control set according to theinvention.

FIG. 5 is another example survey feedback control set according to theinvention.

FIG. 6 is another example survey feedback control set according to theinvention.

FIG. 7 is an example graphical user interface for a couponadministration interface according to the invention.

FIG. 8 is another example graphical user interface for a couponadministration interface according to the invention.

FIG. 9 is an example graphical user interface for a couponadministration interface according to the invention.

FIG. 10 is an example coupon interface according to the invention.

FIG. 11 is an example interface for mood administration according to theinvention.

FIG. 12 is a graphical user interface for a survey interface accordingto the present invention.

FIG. 13 is a schematic diagram of a system embodiment of the invention.

FIG. 14 is a schematic diagram of another system embodiment of theinvention.

DETAILED DESCRIPTION

Referring now to the drawings in general, the illustrations are for thepurpose of describing a preferred embodiment of the invention and arenot intended to limit the invention thereto.

The present invention provides systems and methods for determining userpreferences, identifying users based on psychographic preferences andsuggesting items to users based on those preferences.

In the present description, entities that can be rated are termed items,whereas entities that apply ratings to items are termed raters.Collaborative Filtering (CF) systems employed in the present inventionuse some form of preference model as a way of making suggestions tousers based on their predicted preferences. The present invention usesFactor Analysis to create this preference model. Factor Analysis (FA) isan approach for building a preference model that requires far fewercalculations to make a suggestion as compared to competing CollaborativeFiltering approaches. The preference model uses observed data (contentratings) to predict the likelihood of a rater preferring one unrateditem over another.

An n-dimensional FA-based preference model measures preferences in termsof n separate dimensions. The number of dimensions in a given model (n)holds for the whole model. So if n is 10, the model will characterizeeach rater and each item in terms of 10 dimensional values. A factor isa dimension in an n-dimensional FA model.

Item factors are dimensional measurements characterizing items in ann-dimensional FA model. Rater factors are dimensional measurementscharacterizing raters in an n-dimensional FA model.

A preference vector is a set of n dimensional value measurements,derived from a set of ratings, characterizing preferences for items inan n-dimensional FA-based preference model. A preference vector can becalculated for any non-empty set of ratings that does not contain morethan one rating for the same item.

A whole-rater preference vector is calculated using all of the ratingsin the model applied by that rater.

A mood preference vector is calculated using a subset of ratings appliedby a rater and marked as associated with a distinct rater state(location, time of day, day of week, a user-specified “mood” etc).Having subsets of ratings allow for a rater to have conflicting ratingsfor an item, dictating different preferences in different contexts.

A brand preference vector is a preference vector where the rater, suchas a brand manager, product manager or advertising firm for the brand,rates content on behalf of the brand's preferences. Brands can havemoods, just as any other rater.

A user preference vector is a preference vector where the rater is aspecific user of a content suggestion system. Users can have moods, justlike any other raters.

An adopted preference vector is a preference vector used by an entitythat is not based on that entity's ratings.

A chimeric preference vector is a preference vector calculated from acombination of ratings associated with different raters and/or moods.

A popular preference vector is a preference vector that is frequentlyadopted.

A “highly-adoptive” user is a user that frequently uses an adoptedpreference vector.

The present invention uses factor analysis to build a preference modelfor users. The model can be used in a variety of manners, including formaking suggestion to user(s) based on their predicted preferences. Usersrate content to create a dataset of rated content, which the system usesto create a preference model, which is then used to generate preferencevectors for each user. The present invention differs from prior artmethods in that it provides for creating moods and chimeric vectors. Achimeric vector is created by first identifying a multiplicity of userdatasets of rated items to be used to create the chimeric vector. Onceidentified, the datasets are automatically combined to form a combineddataset; items with ratings collisions are automatically identified andtreated to generate a treated data subset of the combined dataset. Achimeric vector is then generated based on the treated data subset.

The method step of treating the items with ratings collisions includessuch treatments as: using one rating while ignoring other collidingratings, ignoring all colliding ratings, averaging colliding ratings,converting differently-rated items to a strong dislike (to avoid contentthat is likely to highlight differences in preference), and so on.

Any set of ratings can be used to predict preferences for items byraters. A set of ratings associated with a particular context predictspreferences for items in that context. A ratings context could includeall the ratings applied by a rater (generating a whole-rater preferencevector), or it might include only the ratings applied by that raterwhile in a specific mood (generating a mood preference vector). Therater can be a person or a brand. In the case of brands, the moods canbe created and the actual ratings applied by someone responsible for thebrand, such as a product or brand manager or advertising manager. A setof ratings associated with an item creates an item context, which can beused to predict which raters or rater moods will like or dislike thatitem. Also, two sets of ratings can be taken to see how well theymatch—which is to say how well the predictions made with vectors fromeach will match.

In the method described, at least one of the datasets may be a subset ofrated items. Also, at least one dataset may be by a rater and at leastone dataset a dataset of ratings for a rated item. The rated item mayfurthermore be a corporate commercial entity or brand. Thus, the presentinvention can be used to create chimeric vectors for a variety of uses.For example, chimeric vectors can be created for brands, marketpositions, characters, products, actors, celebrities, personalities,politicians, leaders, and the like.

As a specific example, the present invention can be used to create avector for a new, aspirational market position for a business orbusiness product or service. The method steps include: indicating adataset of rated items for a current market position; indicating adataset of rated items for a target market position; automaticallycombining the datasets; identifying the rating collisions; treating thecollisions and automatically generating a treated data subset of thecombined dataset; automatically creating a new market position vectorfrom the treated data set; and automatically identifying content itemsto suggest to users and those users who are most likely to adopt the newmarket position to receive the suggested content items during thetransition from the existing market position to the new market position.

The combined datasets can be treated in a variety of ways to create thetreated data subset. For example, ratings collisions where most or allratings were positive can retain the positive rating. Collisions wheremost or all ratings were negative can retain a negative rating.Collisions where ratings strongly disagree can be weighted with anegative rating, such that the rated item and similar content is notoffered as preferred content.

The present invention includes a system for surveying preferences,generating a preference model, and determining preferred offerings. Thesystem includes a client device for receiving preference ratings fromthe user and forwarding them to the server, a server for hosting thepreference modeling software and ratings database, and addressable URIs.The client device and server are in communication over an network andthe URIs' content is retrievable over the network.

The client UI has selectable feedback controls for providing feedbackfrom the user; the server automatically receives and stores ratings fromthe clients, tracks URIs, uses preference ratings to create a preferencemodel for URIs, and uses the preference model to suggest URIs to users.All the ratings generated by the client are available to the preferencemodel.

An example client UI is shown in FIG. 1. The client UI, generally shownas 100, is shown in the example as a sidebar, separate from the surveycontent 105; however, other orientations are possible, such as at thetop or bottom of the content, free-floating and the like. The UI caninclude a mood button 110 for selecting or creating a mood; a statusicon 120 (a star in the example); a “suggest content” button 130; ashare/comment button 140, a “like” button 150, and a “dislike” button160. An optional channel selection control 170 can be included. Thechannel selection control allows user to indicate that suggestionsshould be limited to a specific area of interest (a channel).Preferably, controls can be used in multiple ways (e.g., be clicked,pressed and held, right-clicked, or pressed in combination with anotherpress on a multi-touch capable device) to provide other related optionsto the user. Another example UI is shown in FIG. 2. In this embodiment,the “$” button 175 navigates the UI to coupon mode. The “gear” button160 navigates to mood selection or a general settings page that includesmood selection. The “up arrow” button 180 is a toggle for showing thetop bar in browsing mode.

A record in the ratings database includes a rated item URI and orratings for a set of related item URIs, a rater having a uniqueidentifier, at least one rating value provided by the rater for therated item, and at least one metadatum for creating subsets of ratings.For example, restricting ratings to those with values in a specificrange for at least one metadatum would define a mood subset that couldthen be used to create a mood preference vector. Examples of moodpreference vectors include those based on ratings subsets defined byautomatically applied rating metadata (client device type, location,time of day, day of week, month, season, and so on) and oruser-specified metadata such as a chosen “mood.”

Preferably, the feedback controls are independent of displayed contentidentified by the URIs and are dynamically assignable by the server. Inthe case of displaying survey content, for instance, the survey creatorwill be able to choose which feedback controls are displayed with eachitem in the survey. Thus, the client includes feedback controls whichare independent of the content, and which are dynamically alterable tobe best suited to collecting opinion on the content in question.

The present invention further includes a survey administration system.The survey administration system described herein uses these and furtherdescribed systems and methods to create a complete, incentivized,targeted content delivery and opinion gathering system, especiallyoptimized for maximizing response rates of mobile device users.

The system according the present invention works with existing,widely-deployed hardware, operating systems, software distributionsystems and data networks. The system applications are preferablyaccessible through the Internet or similar network.

The per-potential-respondent-contacted costs should be similar to thosefor web-based, self-administered surveys. However, substantiallyimproved targeting based on respondent location, demographic,psychographic and social graph criteria should be able to improveresponse rates as well. This combination should reduce costs and reducethe time required to gather sufficient results for analysis.

The system simplifies the creation of surveys by providing templates andusing URLs to provide survey content. Templates are provided fordifferent kinds of surveys based on the order the survey items are to bedisplayed to respondents, including for sequential and decisiontree/adaptive survey types. Survey designers fill in the templates,entering text or URLs to define survey items and selecting from severalavailable feedback control sets, e.g., buttons or sliders: [true,false]; [like, no opinion, dislike]; [−2, −1, 0, +1, +2]; [A, B, C, D,E]; etc). Examples of control sets are shown in FIGS. 3, 4, 5, and 6.FIG. 3 is an example of survey mode where the survey designer wants asimple “like” or “dislike” response. The “EXIT” button exits the survey.The “NEXT” button stores the current response and moves on to the nextsurvey item. FIG. 4 is an example of survey mode for an item where thesurvey designer wants the respondent to choose one of multiple offeredchoices, in this case labeled “A”, “B”, “C”, “D”, and “E”. FIG. 5 is anexample of survey mode for an item where the survey designer wants therespondent to gauge their reaction between two extremes using a sliderUI control. In this case the extremes are “DO NOT AGREE” and “AGREE”,but these can also be set by the survey designer. FIG. 6 is an exampleof survey mode for an item where the survey designer wants therespondent to rate the content using a 0 to 5 star rating.

URLs identifying content containing trademarked or copyrighted materialmay be used with substantially reduced risk of raising objections fromrights-holders as the content will be shown completely and withoutmodification.

The system simplifies the administration of surveys, providing extremelysimple, self-service, automated tools for: selecting the number ofrespondents required with particular characteristics (based ondemographic, psychographic, location and or social graph data);selecting the analyses to run once sufficient responses are collected;displaying the estimated survey administration costs and/or the expectedsurvey time and updating them as options are changed; paying for andinitiating administration of an entered and targeted survey; monitoringthe progress of surveys as they are being administered, including numberof respondents collected, both overall and by targeted group. The systemtakes care of the remaining tasks, eliminating the need for a great dealof technical or domain-specific expertise normally required toadminister the survey.

The present invention works on full-sized desktop and laptop computers,but is preferably optimized for use on mobile devices, e.g. smartphones, netbooks, and tablets. Current device and network limitationsare taken into account when suggestions are made. When available,current location is taken into account when suggestions are made. The UIis designed and configured to minimize the user input required to accessfunctionality.

By using the system to receive suggestions and rate content, the user isvoluntarily submitting data that builds and improves the system'spsychographic profile of them—that is what they like and dislike. Thesystem maintains longitudinal data, thus improving the psychographicprofile. The same system can be used to target surveys and promotionsbased on what users like. The psychographic model that helps predictwhich content the user is more likely to enjoy can also be used to helptarget a survey; for example, suggesting a survey about boats to userswho are interested in boating.

The system further provides enticements to add demographic,psychographic, location and social graph data as well as to take surveysin the form of a points-based rewards program. Points are given fordoing anything that builds or extends the user profile with useful data,for example, adding demographic details (age, sex, zip code, etc.);connecting to social networks or social media services; viewingsuggested content; rating content; sharing content; taking surveys andthe like.

Points determine visible status indications (e.g.—“level” iconsassociated with the user). Gaining more points leads to having a higher“level” and or more impressive “level” icons associated with the user.This encourages the user to consider participation in the system in thecontext of a game and rewards them for taking desired actions. In theexample shown in FIG. 1, the status icon (120) is a star. As the userrates more items the star gets bigger and/or changes color, progressingthrough silver, gold, platinum, etc.

Points are also offered as a generic enticement for taking surveys.While generic as an enticement, points are easily adjusted dynamically.Fewer points can be offered as an incentive to potential respondents inwell-represented target groups. More points can be offered to potentialrespondents in poorly represented target groups.

The system provides a way to convert accumulated points into real worldbenefits through the use of coupons. Some “standard” coupons areavailable to all users for a fixed number of points. Other “bonus”coupons can be more dynamic. For example, coupons or extra points may bemade available only to users who meet specific criteria (users withdemographic, psychographic or social graph characteristics or who havecompleted particular surveys); coupons or other rewards or motivationaldevices may be made available for differing quantities of pointsdepending on specific criteria (as above); and/or coupons or otherrewards or motivational devices may be offered pro-actively for few orno points (“pushed”) to targeted users as part of a brand promotioneffort. These can be issued based on a variety of factors, for example,coupons can be issued based on the psychographic or demographics of auser, their social graph characteristics, which survey has been taken,and/or what the survey results were.

Thus, a method for providing targeted motivational devices to a raterincludes the steps of: providing a system for providingpreference-driven offerings, the system comprising a client device, aserver and addressable URIs; the client device, server and URIs incommunication over an electronic network and the URIs retrievable overthe network; the server automatically receiving and storing ratings,tracking URIs, using ratings to create a preference model for URIs, andusing the preference model to suggest URIs; wherein the stored ratingsinclude at least one record having a rated item URI, a rater having aunique identification, at least one rating value provided by the rater,and at least one metadatum for creating subsets of ratings; and whereinall ratings are available to the preference model; identifying a firstrater with a preference vector; comparing the preference vector of therater with the preference vectors of at least one item with a motivationdevice; identifying candidate items most likely to be preferred by therater; pushing at least one of the motivational devices for thecandidate items to the rater; thereby providing a method for pushingmotivational devices to raters.

Users can also be alerted to surveys and/or surveys with rewards thatmay be of interest to them.

As an example of push surveying according to the present invention, abrand manager wishing to determine the acceptability of a new productfirst determines the psychographic profile of a user who likes thebrand. The brand manager then determines a motivational device that willmotivate users and or users in particular moods matching such a profileand creates a survey designed to determine the user preference for thenew product. The survey administrator then pushes the survey andmotivational device information to users who prefer the brand.

Coupons, rewards and motivational devices can be redeemed in a varietyof manners. For example, coupons may be printed out and taken to a pointof sale; delivered directly to a mobile device and stored on that devicesuch that they may be scanned at a point of sale directly from thedisplay of the mobile device or forwarded as an email or to a fax.

Coupons are uniquely identified, and the system according to the presentinvention provides a tool for merchants to check whether a coupon islegitimately issued and has not been previously used. The system canalso integrate with existing customer rewards programs by associatingmerchant-issued user IDs with the coupon. These IDs can be added to thecoupon as scannable codes or can be returned automatically by the couponvalidation tool. FIG. 7 shows an example coupon user interface,generally described as 200, according to the present invention. Couponscan be stored on the local device in a manner that does not requirenetwork access to display them and thus can be displayed directly on thedevice at a point of sale even if the network coverage is not availablethere. Coupons may be stored for future retrieval according to moods, asshown in the mood tree 210. A coupon 220 includes a unique identifier230 and preferably an expiration date 240. The interface also includesbuttons to view the coupon 250, print the coupon 260 and delete thecoupon 270, and the like.

Another example coupon interface is shown in FIG. 8, showing the list ofmoods that have valid coupons associated with them. The “exit” buttonnavigates back to the browsing mode. Selecting a mood in the list showsthe coupons associated with that mood, as shown in FIG. 9.

FIG. 9 shows a list of 3 coupons associated with the user's “home” mood.The “home” button navigates back to the list of mood with coupons (asshown in FIG. 8). The exit button exits coupon mode, navigating back tobrowsing mode. The “edit” button allows the user to change the order ofthe coupons and/or delete them from this list. Clicking on a coupon listentry shows the coupon itself, as shown in FIG. 10.

FIG. 10 shows an actual coupon. The “coupons” button navigates back tothe previous screen (the coupon list). The “exit” button navigates backto browsing mode. The “mark as used” button is used when the coupon isused at a point of sale. Clicking anywhere on the coupon toggles whetherthe control bars and buttons are shown in this view.

The present invention preferably is integrated with social networkingsystems. This integration provides a benefit to the users in that itenables the sharing of content with friends and followers on any or allconnected social networking systems or other communication systems suchas email with a minimum of UI interaction. For instance, the preferredembodiment enables the user to press one button (“share”), then enter acomment, then press one more button (“OK”) to have a link to the contentalong with their entered comment instantly posted to both their Facebookwall and their Twitter account.

The preferred embodiment would also use information about what content auser has shared with their friends and what their friends have sharedwith the user as a source of potential content to suggest, as data usedto adjust the choice of which items users are more likely to prefer, andas a filter to avoid suggesting content that a user has already seen.For example, if a user had shared an item on Facebook independently ofthe present system, they have probably already seen it, and suggestingit would be redundant. Another example would be if three friends had allsuggested an item that the system expects the user would like but hasnot yet seen, then the system might be more or less inclined to suggestthat item over another that none of the user's friends had suggested.

The preferred embodiment would also use “social graph” data, both toenable access to particular content and to add to the user's profile forpurposes of targeting both content suggestions and surveys. In terms ofenabling access, one example would be making an exclusive content“channel” available only to users who had connected a Facebook accountto the system and who were “fans” of a specific Facebook page. In termsof targeting based on social graph metrics, a survey could be madeavailable only to users with a Twitter account connected that currentlyhas more than 1000 followers.

The present invention provides a content discovery, rating and sharingservice. It suggests content to users based on user interests and pastcontent ratings (likes and dislikes). Rating content (suggested orotherwise found) improves the quality and personalization of the contentsuggestions while building and improving the system's psychographicprofile for the user. Sharing content via multiple connected socialnetworks is enabled with minimal UI interactions, making sharingdiscovered content easier for users while building and improvingdemographic and social graph data for the user in the system. Surveysare one of the kinds of content that the system can suggest, and surveysavailable to a particular user are accessible through the alwaysavailable survey channel.

The present invention provides for subsets of content organized intocontent channels. These content channel subsets can be added to anddynamically assigned by the system or added to and assigned by anadministrator of the system. These content channels can be representedby a control button on the main level or on a sub-level and selected bythe user through the main control button or a sub-level control button.Content channels can include any URI content including web content,images, video, and motivational devices including brand content,promotional materials, incentives, and the like.

The present invention allows users to have different “moods” in whichthey indicate that they are likely to prefer different sorts of content(e.g.—a user might have a “home” and a “work” mood). FIG. 11 shows anexample list of available moods for the user. Clicking on one of themoods in the list selects that mood and returns to browsing mode withthe selected mood now in use. The “new mood” button can add a new moodto the list.

These moods are taken into consideration when suggesting content, andallow a user to denote context-specific differences in their preferencefor certain content. These moods prevent what would otherwise be ratingscollisions due to different preferences in different conditions.

Suggestions are made taking user device and network limitations intoaccount (e.g. Flash videos are not suggested to iOS devices or any videocontent to devices without at least a 3G network connection) in additionto mood and past ratings.

Suggestions can be selectively narrowed by users to content associatedwith a chosen channel—that is a subset of content that might be from acommon source (a brand channel), is functionally related (a “coupons”channel), or is topically related (a “sports” channel). Some channelsare always available, others can be made available when certainconditions are met; having a specific demographic profile, havingfinished a particular survey, and being a fan of a particularorganization on Facebook are all examples of likely conditions. Contentcreators, libraries and distributors can sponsor channels, which containcontent from a subset of items selected by the sponsor. Channels can betransitory, linked to limited-time surveying or promotional efforts.Channels can be dynamically targeted, with a given channel containingdifferent subsets of content for users with differing demographics orpsychographics, for instance. Viewing behavior of channels and ratingsapplied to channel content can help identify interest-basedgroups—“superfans” of a brand, for instance.

The present invention has a special survey mode that optimizes the UIfor administering a survey, especially on a mobile device where spacetends to be more limited and UI interactions tend to be more cumbersome.UI elements that let the user load new content or otherwise navigate maybe hidden or disabled while in survey mode. This includes the elementsto suggest new content, suggest content from a channel, return to theprevious page, and to specify a URL to load. These would lead to a lesscontrolled, potentially out-of-order exposure to the survey items. Inthe place of the disabled or hidden UI elements are two buttons: nextand exit. This ensures the respondent will traverse the survey in theexpected order to whatever extent they complete it. UI elements allowinga wider range of responses than the basic like and dislike buttons mayalso be added. These can be dynamically adjusted amongst availablesurvey response types, including: just the like and dislike buttons(which may also be used for “true” and “false” responses); a multipleposition slider with the like and dislike buttons moving the slider upand down respectively; a set of labeled buttons (“A”, “B”, “C”, “D”,“E”); the UI elements for registering responses and for survey traversalare part of the client application, not part of the web page shown inthe content pane.

An example survey UI is shown in FIG. 12. In this survey UI, generallydescribed as 300, a content window 105 presents survey content. Therater then rates the content using the slider 320, sliding the slidertowards the “+” for a more favorable rating and towards the “−” for aless favorable rating. Once the content has been rated, the raterselects the next content button 330. Progression through the survey isshown with a progress bar 340 and numeric index 350. Should the raterdecide to terminate the survey early, he selects the “exit” button.

This separation of survey content from survey controls ensures a moreconsistent user experience for respondents. The separation also ensuresthat survey content is shown completely and without modification,rendered just as it would be in any standard web browser. This reducesthe likelihood of objections being raised by rights-holders whentrademarked or copyrighted content is presented to a respondent fortheir reaction.

The present invention provides tools for market researchers to build,target and administer surveys. Market researchers log into a surveyadministration application, which provides tools to: monitor theprogress of surveys currently underway; adjust targeting criteria forsurveys currently underway; review the results and analysis of pastsurveys; create, target and initiate new surveys.

Each survey monitor page preferably displays the number of respondents,both overall and broken out by targeted group, and provides a link to aretargeting page, enabling adjustments to be made to the targeting forthat in-progress survey.

Completed surveys each have a review page where the final results aredisplayed. Links are provided to pages containing the results ofrequested analyses performed on the collected responses for that survey.

Creating and targeting a new survey is managed through a simple,template-based, self-service interface. Market researchers start byclicking a “new survey” button. They proceed to a targeting page wherethey define the respondents they are interested in by specifyingrequired demographic, location or social graph related criteria. Fromthis page they may also click on a link that allows them to target thesurvey using psychographic profiles. A target psychographic profile canbe defined by selecting content (URLs) that the desired respondentswould be expected to like or dislike. Any preference profile (e.g.—auser profile, a user mood profile, a brand profile, a brand moodprofile, etc) can be used as an exemplar profile, meaning that thesurvey should target potential respondents with profiles similar to thatof the exemplar (i.e.—target users who like and dislike similarcontent). Especially when a target psychographic profile is likely to beused for multiple surveys, the use of an exemplar user is usually fasterand easier than specifying a collection of URLs.

Once targeting is complete, the market researcher proceeds to the surveycreation page. Here they select a survey template, then fill in thesurvey items. Templates are available for sequential surveys (i.e.—allrespondents get the same items in the same order) as well as foradaptive or “response tree” surveys. Items are filled in as either text,HTML fragments or provided as a URL. Text items will be shown torespondents as a minimally formatted web page containing the providedtext. HTML fragments will be shown to respondents contained in a <div>element in a minimally formatted wrapper page. Content specified by URLwill be shown to users complete and unmodified, exactly as if therespondent had entered the URL into a browser by hand or had clicked ona hypertext link directing their browser to load the content specifiedby that URL. Items can be added or removed as needed to construct asurvey of the desired length.

After targeting and survey construction are completed, the marketresearcher has the option of specifying targeted rewards. Targetedrewards enable the survey to serve a dual purpose, both collectinginformation and enabling a highly targeted brand promotion effort.Targeted rewards can be specified as URLs linking to external rewardsprograms or mechanisms. Targeted rewards can be specified as adollar-value equivalent of points (e.g.—$10 in points). Targeted rewardsare given to respondents with specified profile criteria after havingcompleted the survey, as a bonus, over and above the points offeredprior to the survey as an enticement. Targeted rewards are targetedusing the same criteria that are used to target the desired surveyrespondents more broadly. As such the targeted rewards page looks verysimilar to the basic (re)targeting page for the survey. However, thetargeted rewards will usually target a subset of the respondentstargeted by the survey overall.

After targeting and survey construction are completed and the optionaltargeted rewards are defined or bypassed, the market researcher is takento a payment page. Here they are shown the cost for running the surveythey have just created and targeted. They are given the option to payfor and initiate the survey, to edit the survey details, or to save thesurvey for later editing or use.

As shown in FIG. 13, the system, generally described as 400, includes aserver 410 with a processing unit 411. The server 410 is constructed,configured and coupled to enable communication over a network 450. Theserver provides for user interconnection with the server over thenetwork using a client computing device 440 positioned remotely from theserver. Furthermore, the system is operable for a multiplicity of remoteclient devices 460, 470. A user may interconnect through the network 450using a user device such as a computer, personal digital assistant(PDA), mobile communication device, such as by way of example and notlimitation, a mobile phone, a cell phone, smart phone, laptop computer,netbook, a terminal, or any other client device suitable for networkconnection. Also, alternative architectures may be used instead of theclient/server architecture. For example, the server could actually be acluster of load-balanced servers connecting to a central data store, orthe data store could be a cluster of servers or a cloud-based datastorage service. Alternatively both the server and the data store couldreside on a single networked PC acting as a server for an intranet. Thenetwork 450 may be the Internet, an intranet, or any other networksuitable for searching, obtaining, and/or using information and/orcommunications.

The system of the present invention further includes an operating system412 installed and running on the server 410, enabling server 410 tocommunicate through network 450 with the remote, distributed userdevices. The operating system may be any operating system known in theart that is suitable for network communication.

A memory 420 is interconnected with the server 410. The memory 420 maybe integral with server 410 or may be external to the server andinterconnected therewith. A factor analysis software program 422,ratings database 424 and preference model 426 is stored in memory 420.Alternatively, the software handling the factor analysis modeling can berunning on a server that may contain the database, but might also justconnect to it. The factor analysis can also be implemented entirelywithin a database, using triggers and db scripts.

According to an exemplary embodiment, the factor analysis program iscomputer executable code for using information derived from user inputsto maintain the preference model and ratings database. Alternatively,portions of the program or the whole program may be installed on auser's computing device 440.

A user may connect to the server 410 through network 450 from a clientdevice 440. According to an exemplary embodiment, client device 440 is amobile client device. Client device 440 is interconnected to the network450 such as through a modem, an Ethernet card, or the like. A processingunit 444 may be interconnected with a memory 446. The client device 440may also include one or more input/output devices 448, such as a mouse,a keyboard, a printer, and the like interconnected to the processingunit 444. A display 449, 469, 479 may be interconnected with therespective processing units for providing a graphical user interface.

Client device 440 may have a program of instruction 447, such as adriver enabling client device 440 to interconnect with server 410through network 450.

Regarding methods using the system, the user inputs information into thememory 420, the server 410 creates or updates the preference model andratings database.

Another example system embodiment of the present invention isschematically represented in FIG. 14. As shown in this drawing, thesystem includes an application server 511 in communication with adatabase server 425 and via an Apache HTTPd service 512 over a network450 with client devices such as mobile clients 541, desktop clients 542,advertisers/surveyors 543, analytics consumers 544, and autonomousagents 545.

The database server 425 hosts a database 424 running scripts 427.

The application server 511 includes a servlet container 500 whichcontains the client application 510, analytics application 515,ad/survey management application 520, agent services application 525.The server further includes a business model object application 530, alogging app 535, and a file system 540, all in communication with oneanother. The business model objects application is further incommunication with the database via a Hibernate 546 and JDBC application547. The servlet container apps, business model objects, loggingapplication and Hibernate/JDBC are preferably running on a JAVA platform550.

Certain modifications and improvements will occur to those skilled inthe art upon a reading of the foregoing description. The above-mentionedexamples are provided to serve the purpose of clarifying the aspects ofthe invention and it will be apparent to one skilled in the art thatthey do not serve to limit the scope of the invention. All modificationsand improvements have been deleted herein for the sake of concisenessand readability but are properly within the scope of the presentinvention.

What is claimed is:
 1. A system for determining preference, the systemcomprising: a client device, a server and addressable uniform resourceindicators (URIs), the client device and server in communication over anelectronic network and a URI content retrievable over the network; theclient device having selectable feedback controls for obtaining feedbackratings and moods from a user while viewing content in order to providecontext specific suggestions; wherein the feedback controls aredynamically assigned and adjusted by the server based on criteriaavailable to the server; wherein the ratings are user-specified anddependent upon the URI content; wherein the moods are user-specifiedrater states independent of the URI content; and wherein the moodsdenote a context-specific preference; the server automatically receivingand storing the moods and the ratings, tracking the URIs, using theratings to create a preference model for suggesting additional URIs;wherein the stored ratings include at least one record with a rated itemURI, a rater having a unique identification, at least one rating valueprovided by the rater, and at least one metadatum including the moodsfor creating subsets of the ratings based on the moods; and wherein allratings are available to the preference model.
 2. The system of claim 1,wherein the feedback controls are dynamically assignable by the serverbased on a survey creator's selected feedback control set for an item.3. A method for automatically creating a preference vector calculatedfrom ratings and moods of a plurality of raters, the method stepscomprising: a server obtaining ratings and moods generated by a raterwhile viewing content via a client device, the client device havingselectable feedback controls for obtaining the ratings and the moods ofthe user; wherein the feedback controls are dynamically assigned andadjusted by the server based on criteria available to the server;wherein the ratings are user-specified and dependent upon content ofuniform resource indicators (URIs); wherein the moods are user-specifiedrater states independent of the URI content, and wherein the moodsdenote a context-specific preference; the server communicating with theclient device over an electronic network and the server retrievingaddressable URIs over the network; the server automatically receivingand storing the ratings from the client device, tracking the URIs, usingthe ratings to create a preference model for the URIs, and using thepreference model to suggest additional URIs; wherein the stored ratingsinclude at least one record having a rated item URI, a rater having aunique identification, at least one rating value provided by the rater,and at least one metadatum including the moods for creating subsets ofratings; and wherein all ratings are available to the preference model;identifying, by the server, the multiple subsets of ratings for items tobe used; automatically combining, by the server, the multiple subsets ofratings to form a combined dataset; automatically identifying, by theserver, ratings collisions; treating, by the server, the ratingscollisions automatically to generate a treated data subset of thecombined dataset; and generating, by the server, a preference vectorbased on the treated data subset.
 4. The method of claim 3, wherein thestep of treating the ratings collisions is selected from the group oftreatments consisting of: keeping the rating from a favored set anddiscarding any colliding ratings from other sets, keeping the medianrating of the colliding ratings, eliminating all colliding ratings,averaging the colliding rating values to come up with a mean rating,negatively weighting items with colliding ratings that disagree, andcombinations thereof.
 5. The method of claim 3, wherein at least one ofthe subsets of ratings is a subset of rated items.
 6. The method ofclaim 3, wherein at least one of the subsets of ratings is a dataset ofratings for a rated item by at least one of the raters.
 7. The method ofclaim 3, wherein the rater is a brand.
 8. The method of claim 3, whereinthe rater is selected from the group consisting of characters, actors,celebrities, politicians, and leaders.
 9. A method for identifyingcontent for a new market position, the method steps comprising: a serverobtaining ratings and moods generated by a plurality of raters whileviewing content via a plurality of client devices, the plurality ofclient devices having selectable feedback controls for obtaining theratings and the moods of the plurality of raters; wherein the feedbackcontrols are dynamically assigned and adjusted by the server based oncriteria available to the server; wherein the ratings are user-specifiedand dependent upon content of uniform resource indicators (URIs);wherein the moods are user-specified rater states independent of the URIcontent, and wherein the moods denote a context-specific preference; theserver communicating with the plurality of client devices over anelectronic network and the server retrieving addressable URIs over thenetwork; the server automatically receiving and storing the ratings fromthe plurality of client devices, tracking the URIs, using the ratings tocreate a preference model for the URIs, and using the preference modelto suggest additional URIs; wherein the stored ratings include at leastone record having a rated item URIs, a rated item rater having a uniqueidentification, at least one rated item rating value provided by therated item rater, and at least one metadatum including the moods forcreating subsets of ratings; and wherein all ratings are available tothe preference model; indicating, by the server, a set of ratings ofrated items for a current market position; indicating, by the server, aset of ratings of rated items for a target market position;automatically combining, by the server, the sets of ratings to form acombined datasets; identifying, by the server, the items where there areratings collisions; treating, by the server, ratings collisionsautomatically to generate a treated data subset of the combined dataset;automatically creating, by the server, a new market position vector fromthe treated data set; automatically identifying, by the server, items tosuggest to users during the transition to transit them from the existingmarket position to the new market position.
 10. The method of claim 9,further including the step of automatically identifying users to receivethe identified suggested items.
 11. The method of claim 9, wherein thestep of treating the ratings collisions is selected from the group oftreatments consisting of: keeping the rating from a favored set anddiscarding any colliding ratings from other sets, keeping the medianrating of the colliding ratings, eliminating all colliding ratings,averaging the colliding rating values to come up with a mean rating,negatively weighting items with colliding ratings that disagreeing, andcombinations thereof.
 12. A method for providing motivational devices toraters, comprising: a server obtaining ratings and moods generated by aplurality of raters while viewing content via a plurality of clientdevices, the plurality of client devices having selectable feedbackcontrols for obtaining the ratings and the moods of the plurality ofraters; wherein the feedback controls are dynamically assigned andadjusted by the server based on criteria available to the server; theserver communicating with the plurality of client devices over anelectronic network and the server retrieving addressable uniformresource indicators (URIs) over the network; wherein the ratings areuser-specified and dependent upon content of the URIs; wherein the moodsare user-specified rater states independent of the URI content; andwherein the moods denote a context-specific preference; the serverautomatically receiving and storing the ratings from the plurality ofclient devices, tracking the URIs, using the ratings to create apreference model for, and using the preference model to suggestadditional URIs; wherein the stored ratings include at least one recordhaving a rated item URIs, a rater having a unique identification, atleast one rating value provided by the rater, and at least one metadatumincluding the moods for creating subsets of ratings; and wherein allratings are available to the preference model; identifying, by theserver, a first rater with a preference vector; comparing, by theserver, the preference vector of the rater with the preference vectorsof at least one item with a motivation device; identifying, by theserver, non-colliding items; pushing, by the server, at least one of themotivational devices for the non-colliding items to the rater; therebyproviding a method for pushing motivational devices to raters.
 13. Themethod of claim 12, wherein the motivational device is a coupon.